#to see a file
housing <- read.csv("dataSets/landdata-states.csv")
head(housing[1:5])
## State region Date Home.Value Structure.Cost
## 1 AK West 2010.25 224952 160599
## 2 AK West 2010.50 225511 160252
## 3 AK West 2009.75 225820 163791
## 4 AK West 2010.00 224994 161787
## 5 AK West 2008.00 234590 155400
## 6 AK West 2008.25 233714 157458
#graphing a specified column
hist(housing$Home.Value)
#give axis name, ggplot2 package
library(ggplot2)
ggplot(housing, aes(x = Home.Value)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#base colored scatter plot
plot(Home.Value ~ Date,
data=subset(housing, State == "MA"))
points(Home.Value ~ Date, col="red",
data=subset(housing, State == "TX"))
legend(1975, 400000,
c("MA", "TX"), title="State",
col=c("black", "red"),
pch=c(1, 1))
#colored scatter in ggplot
ggplot(subset(housing, State %in% c("MA", "TX")),
aes(x=Date,
y=Home.Value,
color=State))+
geom_point()
#list of available geometric objects
help.search("geom_", package = "ggplot2")
#points, scatter plot, need x and y
hp2001Q1 <- subset(housing, Date == 2001.25)
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = Land.Value)) +
geom_point()
#or...with log of x
ggplot(hp2001Q1,
aes(y = Structure.Cost, x = log(Land.Value))) +
geom_point()
#prediction line
hp2001Q1$pred.SC <- predict(lm(Structure.Cost ~ log(Land.Value), data = hp2001Q1))
p1 <- ggplot(hp2001Q1, aes(x = log(Land.Value), y = Structure.Cost))
p1 + geom_point(aes(color = Home.Value)) +
geom_line(aes(y = pred.SC))
p1 +
geom_point(aes(color = Home.Value)) +
geom_smooth()
## `geom_smooth()` using method = 'loess'
p1 +
geom_text(aes(label=State), size = 3)
#install.packages("ggrepel")
library("ggrepel")
p1 +
geom_point() +
geom_text_repel(aes(label=State), size = 3)
p1 +
geom_point(aes(size = 2),# 2 is not a variable
color="red") # this is fine -- all points red
p1 +
geom_point(aes(color=Home.Value, shape = region))
## Warning: Removed 1 rows containing missing values (geom_point).
#Exercise I
#1-Create a scatter plot with CPI on the x axis and HDI on the y axis.
library("ggrepel")
library(ggplot2)
dat <- read.csv("dataSets/EconomistData.csv")
head(dat)
## X Country HDI.Rank HDI CPI Region
## 1 1 Afghanistan 172 0.398 1.5 Asia Pacific
## 2 2 Albania 70 0.739 3.1 East EU Cemt Asia
## 3 3 Algeria 96 0.698 2.9 MENA
## 4 4 Angola 148 0.486 2.0 SSA
## 5 5 Argentina 45 0.797 3.0 Americas
## 6 6 Armenia 86 0.716 2.6 East EU Cemt Asia
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point()
#2-Color the points blue.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point(color="Blue")
#3-Map the color of the the points to Region.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point(aes(color=Region))
#4-Make the points bigger by setting size to 2
ggplot(dat, aes(x = CPI, y = HDI, size = 2)) + geom_point(aes(color=Region))
#5-Map the size of the points to HDI.Rank
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point( aes(color=Region, size= HDI.Rank))
args(geom_histogram)
## function (mapping = NULL, data = NULL, stat = "bin", position = "stack",
## ..., binwidth = NULL, bins = NULL, na.rm = FALSE, show.legend = NA,
## inherit.aes = TRUE)
## NULL
args(stat_bin)
## function (mapping = NULL, data = NULL, geom = "bar", position = "stack",
## ..., binwidth = NULL, bins = NULL, center = NULL, boundary = NULL,
## breaks = NULL, closed = c("right", "left"), pad = FALSE,
## na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
## NULL
p2 <- ggplot(housing, aes(x = Home.Value))
p2 + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
p2 + geom_histogram(stat = "bin", binwidth=4000)
housing.sum <- aggregate(housing["Home.Value"], housing["State"], FUN=mean)
rbind(head(housing.sum), tail(housing.sum))
## State Home.Value
## 1 AK 147385.14
## 2 AL 92545.22
## 3 AR 82076.84
## 4 AZ 140755.59
## 5 CA 282808.08
## 6 CO 158175.99
## 46 VA 155391.44
## 47 VT 132394.60
## 48 WA 178522.58
## 49 WI 108359.45
## 50 WV 77161.71
## 51 WY 122897.25
ggplot(housing.sum, aes(x=State, y=Home.Value)) +
geom_bar(stat="identity")
#Exercise II
#1-Re-create a scatter plot with CPI on the x axis and HDI on the y axis (as you did in the previous exercise).
library("ggrepel")
library(ggplot2)
dat <- read.csv("dataSets/EconomistData.csv")
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point()
#2-Overlay a smoothing line on top of the scatter plot using geom_smooth.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point() +
geom_smooth()
## `geom_smooth()` using method = 'loess'
#3-Overlay a smoothing line on top of the scatter plot using geom_smooth, but use a linear model for the predictions. Hint: see ?stat_smooth.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point() +
geom_smooth(method = "lm", formula = y ~ x)
#4-Overlay a smoothing line on top of the scatter plot using geom_line. Hint: change the statistical transformation.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point() +
geom_line(stat="smooth")
## Warning: Computation failed in `stat_smooth()`:
## object 'auto' of mode 'function' was not found
#5-BONUS: Overlay a smoothing line on top of the scatter plot using the default loess method, but make it less smooth. Hint: see ?loess.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point() +
geom_smooth(formula = y ~ x, span=0.3)
## `geom_smooth()` using method = 'loess'
p3 <- ggplot(housing,
aes(x = State,
y = Home.Price.Index)) +
theme(legend.position="top",
axis.text=element_text(size = 6))
(p4 <- p3 + geom_point(aes(color = Date),
alpha = 0.5,
size = 1.5,
position = position_jitter(width = 0.25, height = 0)))
p4 + scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"))
p4 +
scale_x_discrete(name="State Abbreviation") +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = "blue", high = "red")
p4 +
scale_color_continuous(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = ("blue"), high = ("red"))
p4 +
scale_color_gradient2(name="",
breaks = c(1976, 1994, 2013),
labels = c("'76", "'94", "'13"),
low = ("blue"),
high = ("red"),
mid = "gray60",
midpoint = 1994)
?muted
#Exercise III
#1.Create a scatter plot with CPI on the x axis and HDI on the y axis. Color the points to indicate region.
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point(aes(color=Region))
#2-Modify the x, y, and color scales so that they have more easily-understood names (e.g., spell out "Human development Index" instead of "HDI").
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point(aes(color=Region))+scale_x_continuous(name="Corruption Perception Index")+scale_y_continuous(name="Human development Index")
#3-Modify the color scale to use specific values of your choosing. Hint: see ?scale_color_manual.
cols <- c("Americas"= "purple","Asia Pacific"= "black","East EU Cemt Asia"= "white","EU W. Europe"= "blue","MENA"= "red","SSA"= "darkgreen")
ggplot(dat, aes(x = CPI, y = HDI)) + geom_point(aes(color=Region))+scale_x_continuous(name="Corruption Perception Index")+scale_y_continuous(name="Human development Index")+scale_color_manual(values = cols)
p5 <- ggplot(housing, aes(x = Date, y = Home.Value))
p5 + geom_line(aes(color = State))
(p5 <- p5 + geom_line() +
facet_wrap(~State, ncol = 10))
p5 + theme_linedraw()
p5 + theme_light()
p5 + theme_minimal() +
theme(text = element_text(color = "turquoise"))
#opts.png
theme_new <- theme_bw() +
theme(plot.background = element_rect(size = 1, color = "blue", fill = "black"),
text=element_text(size = 12, family = "Serif", color = "ivory"),
axis.text.y = element_text(colour = "purple"),
axis.text.x = element_text(colour = "red"),
panel.background = element_rect(fill = "pink"),
strip.background = element_rect(fill = ("orange")))
p5 + theme_new
housing.byyear <- aggregate(cbind(Home.Value, Land.Value) ~ Date, data = housing, mean)
ggplot(housing.byyear,
aes(x=Date)) +
geom_line(aes(y=Home.Value), color="red") +
geom_line(aes(y=Land.Value), color="blue")
library(tidyr)
home.land.byyear <- gather(housing.byyear,
value = "value",
key = "type",
Home.Value, Land.Value)
ggplot(home.land.byyear,
aes(x=Date,
y=value,
color=type)) +
geom_line()
